Reduction of time in quality check
Model accuracy
Better product quality
When two interlocking parts of a car are successfully joined together in a factory, they typically make a satisfying “click” sound. In case of a misconnection, it becomes difficult to debug and correct the error, driving high rework and warranty costs.
The client, an American multinational automaker with plants across the globe, had to rely on their operators to manually check for the electrical connector clicks which is time intensive and can result in errors. Therefore, they wanted an automated and scalable audio classification solution which could detect the click sound automatically and operate well on a plant floor.
Python
Google Cloud Platform
AutoML
Quantiphi built a Convolution Neural Network (CNN) classification model to detect the click sound in real-time. The CNN-based architecture is capable of separating irrelevant background noise, distinguishing multiple target sounds, and identifying the exact time of the electrical connector click.
